From d4a653411bb3212a47b770471904379a9d15fd20 Mon Sep 17 00:00:00 2001 From: RoyalSkye Date: Thu, 25 May 2023 15:23:59 +0800 Subject: [PATCH] update README --- L2D/README.md | 6 +++- L2D/code/supervised_maml.py | 2 +- README.md | 55 +++++++++++++++++++++++++++++++++++- imgs/overview.png | Bin 0 -> 468168 bytes 4 files changed, 60 insertions(+), 3 deletions(-) create mode 100644 imgs/overview.png diff --git a/L2D/README.md b/L2D/README.md index 0d2ab3f..f42c511 100644 --- a/L2D/README.md +++ b/L2D/README.md @@ -9,7 +9,11 @@ #### 1. Meta-learning implementation: -Please ref to supervised_maml.py +Please ref to `supervised_maml.py` + +*Note: we do not implement the task scheduler since the data generation process in L2D is not flexible as POMO.* + +*Note: based on our exps, the training of the first-order approximation is not stable in L2D. Nevertheless, unlike POMO, the second-order one for L2D is computationally efficient.* #### 2. How to run? diff --git a/L2D/code/supervised_maml.py b/L2D/code/supervised_maml.py index 65280de..7b77166 100644 --- a/L2D/code/supervised_maml.py +++ b/L2D/code/supervised_maml.py @@ -571,7 +571,7 @@ def log(text, **kwargs): if step % args.n_step_generate == 0 and (step > 0 or args.generate_step_zero): generate(args, d_generate, net, step) if step == args.n_steps: break - if step > 10000: # simple curriculm learning strategy + if step > 10000: # simple curriculm learning strategy, note: could simply use random task scheduler instead, start, end = 75000, d.N opt.zero_grad() diff --git a/README.md b/README.md index b207469..e158513 100644 --- a/README.md +++ b/README.md @@ -1 +1,54 @@ -# TBA \ No newline at end of file +

Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

+ +

+ Paper    License        Paper +

+ +The PyTorch Implementation of *ICML 2023 Poster -- "Towards Omni-generalizable Neural Methods for Vehicle Routing Problems"* by [Jianan Zhou](https://royalskye.github.io), [Yaoxin Wu](https://research.tue.nl/en/persons/yaoxin-wu), [Wen Song](https://songwenas12.github.io), [Zhiguang Cao](https://zhiguangcaosg.github.io), [Jie Zhang](https://personal.ntu.edu.sg/zhangj). + +

+ +### TL;DR + +This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution (a.k.a. omni-generalization) of neural methods in VRPs. Technically, a general meta-learning framework is developed to tackle it. + +### TODO + +- [ ] Finish Dependencies & How to Run. +- [ ] Camera-ready. +- [ ] Slide and Poster. +- [ ] Release Review. + +### Dependencies + + + +### How to Run + + + +### Reviews + +We would like to thank the anonymous reviewers and (S)ACs of ICML 2023 for their constructive comments and recommendation. We will share the reviews later. + +### Acknowledgments + +Thank the following repositories, which are baselines of our code: + +* https://github.com/wouterkool/attention-learn-to-route +* https://github.com/yd-kwon/POMO +* https://github.com/mit-wu-lab/learning-to-delegate + +### Citation + +If you find our paper and code useful, please cite our paper: + +```tex +@inproceedings{zhou2023towards, +title ={Towards Omni-generalizable Neural Methods for Vehicle Routing Problems}, +author ={Jianan Zhou and Yaoxin Wu and Wen Song and Zhiguang Cao and Jie Zhang}, +booktitle ={International Conference on Machine Learning}, +year ={2023} +} +``` + diff --git a/imgs/overview.png b/imgs/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..6d6e34228e865586cf9611da50efd9bbe89fe5f3 GIT binary patch literal 468168 zcmeEuWmHt%+dd$ufC@-A7APs*^(Y_>BHb-5(k=2RAfDn6cZoar05rj*14#Sh|8Jc9;bGu!#Op=tFxC(e9%e`!yB#XU+a3p@hi{*E|-&HVp6MYMLZ-HYZ$&;R#x`u 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